{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building a LAS file from scratch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import lasio\n",
    "\n",
    "import datetime\n",
    "import numpy\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1\n",
    "\n",
    "Create some fake data, and make some of the values at the bottom NULL (``numpy.nan``). Note that of course every curve in a LAS file is recorded against a reference/index, either depth or time, so we create that array too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "depths = numpy.arange(10, 50, 0.5)\n",
    "fake_curve = numpy.random.random(len(depths))\n",
    "fake_curve[-10:] = numpy.nan   # Add some null values at the bottom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xafdd2b0>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tQP6AqhDcjjVr2Hc/6aTZx9hU7iMj3LasjoAqnCJ3Sa5lee4vhwoVm1Lue/dW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\n4Ii5mQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3b07b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(depths, fake_curve)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2\n",
    "\n",
    "Create an empty LASFile object and review its header section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "l = lasio.LASFile()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Curves': [],\n",
       " 'Other': '',\n",
       " 'Parameter': [],\n",
       " 'Version': [HeaderItem(mnemonic=VERS, unit=, value=2.0, descr=CWLS log ASCII Standard -VERSION 2.0, original_mnemonic=VERS),\n",
       "  HeaderItem(mnemonic=WRAP, unit=, value=NO, descr=One line per depth step, original_mnemonic=WRAP),\n",
       "  HeaderItem(mnemonic=DLM, unit=, value=SPACE, descr=Column Data Section Delimiter, original_mnemonic=DLM)],\n",
       " 'Well': [HeaderItem(mnemonic=STRT, unit=m, value=nan, descr=START DEPTH, original_mnemonic=STRT),\n",
       "  HeaderItem(mnemonic=STOP, unit=m, value=nan, descr=STOP DEPTH, original_mnemonic=STOP),\n",
       "  HeaderItem(mnemonic=STEP, unit=m, value=nan, descr=STEP, original_mnemonic=STEP),\n",
       "  HeaderItem(mnemonic=NULL, unit=, value=-9999.25, descr=NULL VALUE, original_mnemonic=NULL),\n",
       "  HeaderItem(mnemonic=COMP, unit=, value=, descr=COMPANY, original_mnemonic=COMP),\n",
       "  HeaderItem(mnemonic=WELL, unit=, value=, descr=WELL, original_mnemonic=WELL),\n",
       "  HeaderItem(mnemonic=FLD, unit=, value=, descr=FIELD, original_mnemonic=FLD),\n",
       "  HeaderItem(mnemonic=LOC, unit=, value=, descr=LOCATION, original_mnemonic=LOC),\n",
       "  HeaderItem(mnemonic=PROV, unit=, value=, descr=PROVINCE, original_mnemonic=PROV),\n",
       "  HeaderItem(mnemonic=CNTY, unit=, value=, descr=COUNTY, original_mnemonic=CNTY),\n",
       "  HeaderItem(mnemonic=STAT, unit=, value=, descr=STATE, original_mnemonic=STAT),\n",
       "  HeaderItem(mnemonic=CTRY, unit=, value=, descr=COUNTRY, original_mnemonic=CTRY),\n",
       "  HeaderItem(mnemonic=SRVC, unit=, value=, descr=SERVICE COMPANY, original_mnemonic=SRVC),\n",
       "  HeaderItem(mnemonic=DATE, unit=, value=, descr=DATE, original_mnemonic=DATE),\n",
       "  HeaderItem(mnemonic=UWI, unit=, value=, descr=UNIQUE WELL ID, original_mnemonic=UWI),\n",
       "  HeaderItem(mnemonic=API, unit=, value=, descr=API NUMBER, original_mnemonic=API)]}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.header"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's add some information to the header: \n",
    "\n",
    "- the date\n",
    "- the operator (in the Parameter section)\n",
    "- a description of the file in the Other section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's change the date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "l.well.DATE = str(datetime.datetime.today())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's make a new item in the ~Parameters section for the operator. To do this we need to make a new ``HeaderItem``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "l.params['ENGI'] = lasio.HeaderItem(\"ENGI\", \"\", \"kinverarity@hotmail.com\", \"Creator of this file...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And finally, add some free text to the ~Other section:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "l.other = \"Example of how to create a LAS file from scratch using lasio\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3\n",
    "\n",
    "Add the curves to the LAS file using the ``add_curve`` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "l.add_curve('DEPT', depths, unit='m')\n",
    "l.add_curve('FAKE_CURVE', fake_curve, descr='fake curve')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4\n",
    "\n",
    "Now let's write out two files: one according to the LAS file specification version 1.2, and one according to 2.0. Note that by default an empty ``LASFile`` object is version 2.0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "fn = \"scratch_example_v2.las\"\n",
    "with open(fn, mode=\"w\") as f: # Write LAS file to disk\n",
    "    l.write(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and let's see if that worked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~Version ---------------------------------------------------\n",
      "VERS.   2.0 : CWLS log ASCII Standard -VERSION 2.0\n",
      "WRAP.    NO : One line per depth step\n",
      "DLM . SPACE : Column Data Section Delimiter\n",
      "~Well ------------------------------------------------------\n",
      "STRT.m                      10.0 : START DEPTH\n",
      "STOP.m                      49.5 : STOP DEPTH\n",
      "STEP.m                       0.5 : STEP\n",
      "NULL.                   -9999.25 : NULL VALUE\n",
      "COMP.                            : COMPANY\n",
      "WELL.                            : WELL\n",
      "FLD .                            : FIELD\n",
      "LOC .                            : LOCATION\n",
      "PROV.                            : PROVINCE\n",
      "CNTY.                            : COUNTY\n",
      "STAT.                            : STATE\n",
      "CTRY.                            : COUNTRY\n",
      "SRVC.                            : SERVICE COMPANY\n",
      "DATE. 2016-02-13 16:52:39.445000 : DATE\n",
      "UWI .                            : UNIQUE WELL ID\n",
      "API .                            : API NUMBER\n",
      "~Curves ----------------------------------------------------\n",
      "DEPT      .m  : \n",
      "FAKE_CURVE.   : fake curve\n",
      "~Params ----------------------------------------------------\n",
      "ENGI. kinverarity@hotmail.com : Creator of this file...\n",
      "~Other -----------------------------------------------------\n",
      "Example of how to create a LAS file from scratch using lasio\n",
      "~ASCII -----------------------------------------------------\n",
      "         10   0.026008\n",
      "       10.5   0.056402\n",
      "         11    0.58473\n",
      "       11.5   0.062889\n",
      "         12   0.063426\n",
      "       12.5    0.38872\n",
      "         13    0.75342\n",
      "       13.5    0.93299\n",
      "         14    0.70876\n",
      "       14.5    0.21405\n",
      "         15      0.948\n",
      "       15.5    0.96069\n",
      "         16    0.95769\n",
      "       16.5    0.47167\n",
      "         17    0.92044\n",
      "       17.5    0.45118\n",
      "         18    0.41889\n",
      "       18.5    0.42029\n",
      "         19    0.30528\n",
      "       19.5    0.15235\n",
      "         20   0.088225\n",
      "       20.5    0.38494\n",
      "         21   0.026402\n",
      "       21.5    0.55415\n",
      "         22    0.33504\n",
      "       22.5    0.27227\n",
      "         23    0.15694\n",
      "       23.5    0.67574\n",
      "         24    0.61331\n",
      "       24.5    0.39824\n",
      "         25    0.12945\n",
      "       25.5    0.14877\n",
      "         26    0.93639\n",
      "       26.5     0.2013\n",
      "         27    0.76118\n",
      "       27.5    0.44274\n",
      "         28  0.0078593\n",
      "       28.5    0.86707\n",
      "         29   0.077415\n",
      "       29.5    0.61982\n",
      "         30    0.92987\n",
      "       30.5     0.4554\n",
      "         31    0.90304\n",
      "       31.5    0.23425\n",
      "         32    0.85763\n",
      "       32.5    0.03689\n",
      "         33    0.53212\n",
      "       33.5    0.48849\n",
      "         34    0.55165\n",
      "       34.5    0.55116\n",
      "         35  0.0071184\n",
      "       35.5    0.16983\n",
      "         36    0.82744\n",
      "       36.5    0.80642\n",
      "         37    0.46575\n",
      "       37.5    0.91243\n",
      "         38    0.72884\n",
      "       38.5    0.94173\n",
      "         39    0.16294\n",
      "       39.5    0.97191\n",
      "         40    0.25037\n",
      "       40.5    0.57772\n",
      "         41    0.40011\n",
      "       41.5    0.63539\n",
      "         42    0.70898\n",
      "       42.5   0.042878\n",
      "         43    0.20613\n",
      "       43.5    0.25082\n",
      "         44    0.75637\n",
      "       44.5    0.87049\n",
      "         45   -9999.25\n",
      "       45.5   -9999.25\n",
      "         46   -9999.25\n",
      "       46.5   -9999.25\n",
      "         47   -9999.25\n",
      "       47.5   -9999.25\n",
      "         48   -9999.25\n",
      "       48.5   -9999.25\n",
      "         49   -9999.25\n",
      "       49.5   -9999.25\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open(fn, mode=\"r\") as f: # Show the result... \n",
    "    print(f.read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xb4d0a90>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tQP6AqhDcjjVr2Hc/6aTZx9hU7iMj3LasjoAqnCJ3Sa5lee4vhwoVm1Lue/dW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\n4Ii5mQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb46eb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(l['DEPT'], l['FAKE_CURVE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "os.remove(fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
